Air pollution is ubiquitous but very little is currently known about adverse effects it may have on the aging brain. Recently it has been hypothesized that air pollution may act on biologic pathways contributing to Parkinson's disease (PD), the second most common neurodegenerative disorder engendering great human cost in aging populations. However, to date there is no human data available to test this novel hypothesis. To fill this gap we will take advantage of our unique resource created with NIEHS funding over the past 6 years in Denmark, the Parkinson in Denmark (PASIDA) study, and test the novel hypothesis that long-term exposure to traffic-related air pollution increases the risk of PD. The PASIDA study recruited 1,867 cases with confirmed idiopathic PD and 1,920 age-sex matched population controls for whom we have available genotyping information, livelong behavioral risk factor and occupational exposure data from interviews, and additionally data extracted from National Danish hospital and outpatient clinic records and drug prescription histories. We now propose to use a sophisticated and validated GIS-based dispersion model, AirGIS, to assess exposure to traffic-related air pollution in PASIDA participants. Our collaborator, Dr. Raaschou-Nielsen, employed this model successfully to assess cancer risk from air pollution (Raaschou-Nielsen et al '11 a, b). AirGIS allows us to examine long-term air pollution with high temporal and spatial resolution creating address level individual exposure measures for as many as 39 years while accounting for residential mobility. Exposure is modeled in AirGIS based on nitrogen dioxides (NO2)/nitrogen oxides (NOx) which serve as indicators for air pollution mixtures from traffic. Furthermore, we will also be able to examine whether the effects of air pollution on PD development are influenced by genetic variation in two proinflammatory cytokines that actively regulate a broad spectrum of neuroinflammatory responses; we have previously reported that functional variants of these cytokine are associated with PD risk (Wahner et al '07). Thus, our specific aims are to: (1) assess the influence of long-term traffic-related air pollution exposure on PD risk for 1,867 cases and 1,920 population controls combining existing PASIDA data with new exposure measures from AirGIS; and (2) investigate the combined action of air pollution and genetic variants in inflammatory genes previously linked to PD. The proposed study uses complex and validated models to estimate individual level air pollution exposures that account for long term address histories in a large, well-designed population-based study for which we collected extensive risk factor and genetic information. These resources make our existing PASIDA study a strong and efficient platform for addressing possible air pollution impacts on PD. Our focus on long- term traffic-related air pollution, an ubiquitous exposure, will provide data in support of public health policy and air pollution regulation, improve prevention efforts aimed at eliminating detrimental toxins from the environment, and possibly have broad implications for the prevention of neurodegeneration in aging populations.